Adaptive nonequilibrium design of actin-based metamaterials: fundamental and practical limits of control

TL;DR

This paper demonstrates how dynamic, nonequilibrium control of actin networks via reinforcement learning can encode metamaterial properties and memory, highlighting the importance of dissipation and entropy flow in self-assembly.

Contribution

It introduces a systematic approach to control actin-based metamaterials using reinforcement learning, revealing the role of dissipation and entropy in encoding memory.

Findings

Actin networks can be encoded with metamaterial properties through external force protocols.

Memory encoding in these networks depends on dissipation and entropy flow.

Reinforcement learning effectively selects protocols for desired network responses.

Abstract

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials and their properties. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize this control or how \emph{controllable} a given material can be. Here, we demonstrate that branched actin networks can be encoded with \textit{metamaterial} properties by dynamically controlling the applied force under which they grow, and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding ``memory'' within these structures. Interestingly, we show that encoding memory requires dissipation and the rate of encoding is constrained by the flow of entropy -- both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures.